{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 载入数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.rcParams['font.sans-serif'] = ['SimHei']\n",
    "plt.rcParams['axes.unicode_minus'] = False\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "target_url = (\"http://archive.ics.uci.edu/ml/machine-\"\n",
    "              \"learning-databases/wine-quality/winequality-red.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fixed acidity</th>\n",
       "      <th>volatile acidity</th>\n",
       "      <th>citric acid</th>\n",
       "      <th>residual sugar</th>\n",
       "      <th>chlorides</th>\n",
       "      <th>free sulfur dioxide</th>\n",
       "      <th>total sulfur dioxide</th>\n",
       "      <th>density</th>\n",
       "      <th>pH</th>\n",
       "      <th>sulphates</th>\n",
       "      <th>alcohol</th>\n",
       "      <th>quality</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7.4</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.076</td>\n",
       "      <td>11.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0.9978</td>\n",
       "      <td>3.51</td>\n",
       "      <td>0.56</td>\n",
       "      <td>9.4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7.8</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.6</td>\n",
       "      <td>0.098</td>\n",
       "      <td>25.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>0.9968</td>\n",
       "      <td>3.20</td>\n",
       "      <td>0.68</td>\n",
       "      <td>9.8</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.8</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.04</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0.092</td>\n",
       "      <td>15.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0.9970</td>\n",
       "      <td>3.26</td>\n",
       "      <td>0.65</td>\n",
       "      <td>9.8</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11.2</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.56</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.075</td>\n",
       "      <td>17.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>0.9980</td>\n",
       "      <td>3.16</td>\n",
       "      <td>0.58</td>\n",
       "      <td>9.8</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7.4</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.076</td>\n",
       "      <td>11.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0.9978</td>\n",
       "      <td>3.51</td>\n",
       "      <td>0.56</td>\n",
       "      <td>9.4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   fixed acidity  volatile acidity  citric acid  residual sugar  chlorides  \\\n",
       "0            7.4              0.70         0.00             1.9      0.076   \n",
       "1            7.8              0.88         0.00             2.6      0.098   \n",
       "2            7.8              0.76         0.04             2.3      0.092   \n",
       "3           11.2              0.28         0.56             1.9      0.075   \n",
       "4            7.4              0.70         0.00             1.9      0.076   \n",
       "\n",
       "   free sulfur dioxide  total sulfur dioxide  density    pH  sulphates  \\\n",
       "0                 11.0                  34.0   0.9978  3.51       0.56   \n",
       "1                 25.0                  67.0   0.9968  3.20       0.68   \n",
       "2                 15.0                  54.0   0.9970  3.26       0.65   \n",
       "3                 17.0                  60.0   0.9980  3.16       0.58   \n",
       "4                 11.0                  34.0   0.9978  3.51       0.56   \n",
       "\n",
       "   alcohol  quality  \n",
       "0      9.4        5  \n",
       "1      9.8        5  \n",
       "2      9.8        5  \n",
       "3      9.8        6  \n",
       "4      9.4        5  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "columns_mapping = {\n",
    "    'fixed acidity': '非挥发性酸',\n",
    "    'volatile acidity': '挥发性酸',\n",
    "    'citric acid': '柠檬酸',\n",
    "    'residual sugar': '残留糖分',\n",
    "    'chlorides': '氯化物',\n",
    "    'free sulfur dioxide': '游离二氧化硫',\n",
    "    'total sulfur dioxide': '总二氧化硫',\n",
    "    'density': '密度',\n",
    "    'pH': 'PH值',\n",
    "    'sulphates': '亚硝酸盐',  # 百度翻译是：硫酸盐\n",
    "    'alcohol': '酒精含量',\n",
    "    'quality': '口感评分'\n",
    "}\n",
    "\n",
    "try:\n",
    "    df_wine = pd.read_csv(\"../../data/wine.csv\", header=0)\n",
    "except Exception as e:\n",
    "    print(e)\n",
    "    df_wine = pd.read_csv(target_url, sep=\";\",header=0, prefix=\"V\")\n",
    "    df_wine.columns = columns_mapping.keys()\n",
    "    df_wine.to_csv(\"../../data/wine.csv\", index=False)\n",
    "\n",
    "df_wine.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 红酒数据的描述性统计"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fixed acidity</th>\n",
       "      <th>volatile acidity</th>\n",
       "      <th>citric acid</th>\n",
       "      <th>residual sugar</th>\n",
       "      <th>chlorides</th>\n",
       "      <th>free sulfur dioxide</th>\n",
       "      <th>total sulfur dioxide</th>\n",
       "      <th>density</th>\n",
       "      <th>pH</th>\n",
       "      <th>sulphates</th>\n",
       "      <th>alcohol</th>\n",
       "      <th>quality</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1599.000000</td>\n",
       "      <td>1599.000000</td>\n",
       "      <td>1599.000000</td>\n",
       "      <td>1599.000000</td>\n",
       "      <td>1599.000000</td>\n",
       "      <td>1599.000000</td>\n",
       "      <td>1599.000000</td>\n",
       "      <td>1599.000000</td>\n",
       "      <td>1599.000000</td>\n",
       "      <td>1599.000000</td>\n",
       "      <td>1599.000000</td>\n",
       "      <td>1599.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>8.319637</td>\n",
       "      <td>0.527821</td>\n",
       "      <td>0.270976</td>\n",
       "      <td>2.538806</td>\n",
       "      <td>0.087467</td>\n",
       "      <td>15.874922</td>\n",
       "      <td>46.467792</td>\n",
       "      <td>0.996747</td>\n",
       "      <td>3.311113</td>\n",
       "      <td>0.658149</td>\n",
       "      <td>10.422983</td>\n",
       "      <td>5.636023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.741096</td>\n",
       "      <td>0.179060</td>\n",
       "      <td>0.194801</td>\n",
       "      <td>1.409928</td>\n",
       "      <td>0.047065</td>\n",
       "      <td>10.460157</td>\n",
       "      <td>32.895324</td>\n",
       "      <td>0.001887</td>\n",
       "      <td>0.154386</td>\n",
       "      <td>0.169507</td>\n",
       "      <td>1.065668</td>\n",
       "      <td>0.807569</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>4.600000</td>\n",
       "      <td>0.120000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>0.012000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>0.990070</td>\n",
       "      <td>2.740000</td>\n",
       "      <td>0.330000</td>\n",
       "      <td>8.400000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>7.100000</td>\n",
       "      <td>0.390000</td>\n",
       "      <td>0.090000</td>\n",
       "      <td>1.900000</td>\n",
       "      <td>0.070000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>0.995600</td>\n",
       "      <td>3.210000</td>\n",
       "      <td>0.550000</td>\n",
       "      <td>9.500000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>7.900000</td>\n",
       "      <td>0.520000</td>\n",
       "      <td>0.260000</td>\n",
       "      <td>2.200000</td>\n",
       "      <td>0.079000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>0.996750</td>\n",
       "      <td>3.310000</td>\n",
       "      <td>0.620000</td>\n",
       "      <td>10.200000</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>9.200000</td>\n",
       "      <td>0.640000</td>\n",
       "      <td>0.420000</td>\n",
       "      <td>2.600000</td>\n",
       "      <td>0.090000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>0.997835</td>\n",
       "      <td>3.400000</td>\n",
       "      <td>0.730000</td>\n",
       "      <td>11.100000</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>15.900000</td>\n",
       "      <td>1.580000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>15.500000</td>\n",
       "      <td>0.611000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>289.000000</td>\n",
       "      <td>1.003690</td>\n",
       "      <td>4.010000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>14.900000</td>\n",
       "      <td>8.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       fixed acidity  volatile acidity  citric acid  residual sugar  \\\n",
       "count    1599.000000       1599.000000  1599.000000     1599.000000   \n",
       "mean        8.319637          0.527821     0.270976        2.538806   \n",
       "std         1.741096          0.179060     0.194801        1.409928   \n",
       "min         4.600000          0.120000     0.000000        0.900000   \n",
       "25%         7.100000          0.390000     0.090000        1.900000   \n",
       "50%         7.900000          0.520000     0.260000        2.200000   \n",
       "75%         9.200000          0.640000     0.420000        2.600000   \n",
       "max        15.900000          1.580000     1.000000       15.500000   \n",
       "\n",
       "         chlorides  free sulfur dioxide  total sulfur dioxide      density  \\\n",
       "count  1599.000000          1599.000000           1599.000000  1599.000000   \n",
       "mean      0.087467            15.874922             46.467792     0.996747   \n",
       "std       0.047065            10.460157             32.895324     0.001887   \n",
       "min       0.012000             1.000000              6.000000     0.990070   \n",
       "25%       0.070000             7.000000             22.000000     0.995600   \n",
       "50%       0.079000            14.000000             38.000000     0.996750   \n",
       "75%       0.090000            21.000000             62.000000     0.997835   \n",
       "max       0.611000            72.000000            289.000000     1.003690   \n",
       "\n",
       "                pH    sulphates      alcohol      quality  \n",
       "count  1599.000000  1599.000000  1599.000000  1599.000000  \n",
       "mean      3.311113     0.658149    10.422983     5.636023  \n",
       "std       0.154386     0.169507     1.065668     0.807569  \n",
       "min       2.740000     0.330000     8.400000     3.000000  \n",
       "25%       3.210000     0.550000     9.500000     5.000000  \n",
       "50%       3.310000     0.620000    10.200000     6.000000  \n",
       "75%       3.400000     0.730000    11.100000     6.000000  \n",
       "max       4.010000     2.000000    14.900000     8.000000  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_summary = df_wine.describe()\n",
    "df_summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1599 entries, 0 to 1598\n",
      "Data columns (total 12 columns):\n",
      "fixed acidity           1599 non-null float64\n",
      "volatile acidity        1599 non-null float64\n",
      "citric acid             1599 non-null float64\n",
      "residual sugar          1599 non-null float64\n",
      "chlorides               1599 non-null float64\n",
      "free sulfur dioxide     1599 non-null float64\n",
      "total sulfur dioxide    1599 non-null float64\n",
      "density                 1599 non-null float64\n",
      "pH                      1599 non-null float64\n",
      "sulphates               1599 non-null float64\n",
      "alcohol                 1599 non-null float64\n",
      "quality                 1599 non-null int64\n",
      "dtypes: float64(11), int64(1)\n",
      "memory usage: 150.0 KB\n"
     ]
    }
   ],
   "source": [
    "df_wine.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 绘制箱线图：使用z-score的数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_wine_normalized = df_wine.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # 原书的的实现，比较麻烦\n",
    "# nrows, ncols = df_wine_normalized.shape\n",
    "# for i in range(ncols):\n",
    "#     mean = df_wine_normalized.iloc[:, i].mean()\n",
    "#     std = df_wine_normalized.iloc[:, i].std()\n",
    "#     df_wine_normalized.iloc[:, i] = (df_wine_normalized.iloc[:, i] - mean)/std\n",
    "\n",
    "# df_wine_normalized.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>fixed acidity</th>\n",
       "      <th>volatile acidity</th>\n",
       "      <th>citric acid</th>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.528194</td>\n",
       "      <td>0.961576</td>\n",
       "      <td>-1.391037</td>\n",
       "      <td>-0.453077</td>\n",
       "      <td>-0.243630</td>\n",
       "      <td>-0.466047</td>\n",
       "      <td>-0.379014</td>\n",
       "      <td>0.558100</td>\n",
       "      <td>1.288240</td>\n",
       "      <td>-0.579025</td>\n",
       "      <td>-0.959946</td>\n",
       "      <td>-0.787576</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.298454</td>\n",
       "      <td>1.966827</td>\n",
       "      <td>-1.391037</td>\n",
       "      <td>0.043403</td>\n",
       "      <td>0.223805</td>\n",
       "      <td>0.872365</td>\n",
       "      <td>0.624168</td>\n",
       "      <td>0.028252</td>\n",
       "      <td>-0.719708</td>\n",
       "      <td>0.128910</td>\n",
       "      <td>-0.584594</td>\n",
       "      <td>-0.787576</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.298454</td>\n",
       "      <td>1.296660</td>\n",
       "      <td>-1.185699</td>\n",
       "      <td>-0.169374</td>\n",
       "      <td>0.096323</td>\n",
       "      <td>-0.083643</td>\n",
       "      <td>0.228975</td>\n",
       "      <td>0.134222</td>\n",
       "      <td>-0.331073</td>\n",
       "      <td>-0.048074</td>\n",
       "      <td>-0.584594</td>\n",
       "      <td>-0.787576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.654339</td>\n",
       "      <td>-1.384011</td>\n",
       "      <td>1.483689</td>\n",
       "      <td>-0.453077</td>\n",
       "      <td>-0.264878</td>\n",
       "      <td>0.107558</td>\n",
       "      <td>0.411372</td>\n",
       "      <td>0.664069</td>\n",
       "      <td>-0.978798</td>\n",
       "      <td>-0.461036</td>\n",
       "      <td>-0.584594</td>\n",
       "      <td>0.450707</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.528194</td>\n",
       "      <td>0.961576</td>\n",
       "      <td>-1.391037</td>\n",
       "      <td>-0.453077</td>\n",
       "      <td>-0.243630</td>\n",
       "      <td>-0.466047</td>\n",
       "      <td>-0.379014</td>\n",
       "      <td>0.558100</td>\n",
       "      <td>1.288240</td>\n",
       "      <td>-0.579025</td>\n",
       "      <td>-0.959946</td>\n",
       "      <td>-0.787576</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   fixed acidity  volatile acidity  citric acid  residual sugar  chlorides  \\\n",
       "0      -0.528194          0.961576    -1.391037       -0.453077  -0.243630   \n",
       "1      -0.298454          1.966827    -1.391037        0.043403   0.223805   \n",
       "2      -0.298454          1.296660    -1.185699       -0.169374   0.096323   \n",
       "3       1.654339         -1.384011     1.483689       -0.453077  -0.264878   \n",
       "4      -0.528194          0.961576    -1.391037       -0.453077  -0.243630   \n",
       "\n",
       "   free sulfur dioxide  total sulfur dioxide   density        pH  sulphates  \\\n",
       "0            -0.466047             -0.379014  0.558100  1.288240  -0.579025   \n",
       "1             0.872365              0.624168  0.028252 -0.719708   0.128910   \n",
       "2            -0.083643              0.228975  0.134222 -0.331073  -0.048074   \n",
       "3             0.107558              0.411372  0.664069 -0.978798  -0.461036   \n",
       "4            -0.466047             -0.379014  0.558100  1.288240  -0.579025   \n",
       "\n",
       "    alcohol   quality  \n",
       "0 -0.959946 -0.787576  \n",
       "1 -0.584594 -0.787576  \n",
       "2 -0.584594 -0.787576  \n",
       "3 -0.584594  0.450707  \n",
       "4 -0.959946 -0.787576  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_wine_normalized = (df_wine_normalized - df_wine_normalized.mean())/df_wine_normalized.std()\n",
    "df_wine_normalized.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(16, 8))\n",
    "ax = fig.add_subplot(111)\n",
    "df_wine_normalized.plot(kind='box', ax=ax)\n",
    "for label in ax.xaxis.get_ticklabels():\n",
    "    label.set_rotation(45)\n",
    "    label.set_fontsize(15)\n",
    "ax.set_xlabel(\"Attribute Index\")\n",
    "ax.set_ylabel(\"Quantile Ranges - Normalized\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(16, 8))\n",
    "ax = fig.add_subplot(111)\n",
    "ax.boxplot(df_wine_normalized.values)\n",
    "ax.set_xlabel(\"Attribute Index\")\n",
    "ax.set_ylabel(\"Quantile Ranges - Normalized\")\n",
    "ax.set_xticklabels([columns_mapping.get(label, label) for label in df_wine_normalized.columns])\n",
    "for label in ax.xaxis.get_ticklabels():\n",
    "    label.set_rotation(45)\n",
    "    label.set_fontsize(15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
